pygauss 0.2.1

The Easy Way (OSX)

The recommended was to use pygauss is to download the Anaconda Scientific Python Distribution (64-bit). Once downloaded a new environment can be created in terminal and pygauss installed:

conda create -n pg_env python=2.7
conda install -c https://conda.binstar.org/cjs14 -n pg_env pygauss

The Middle Road (Linux)

There is currently no pygauss conda distributable for Linux, but there is for chemlab. So chemlab can be installed, then install a few dependancies that pip finds difficult / doesn’t have, and finally install pygauss using pip (make sure to activate the required environment)

conda create -n pg_env python=2.7
conda install -n pg_env -c https://conda.binstar.org/cjs14 chemlab
conda install -n pg_env <pil, pandas, matplotlib, scikit-learn>
activate pg_env
pip install pygauss

The Hard Way (Windows)

There is currently no pygauss conda distributable for Windows or for chemlab which has C-extensions that need to be built using a compiler. Therfore it will need to be cloned from GitHub. the extensions built, dependancies installed and finally installed.

conda create -n pg_env python=2.7
conda install -n pg_env -c https://conda.binstar.org/cjs14 cclib
conda install -n pg_env -c https://conda.binstar.org/cjs14 chemview
conda install -n pg_env -c https://conda.binstar.org/cjs14 pyopengl
git clone --recursive https://github.com/chemlab/chemlab.git
cd chemlab
python setup.py build_ext --inplace
conda install -n pg_env <pil, pandas, matplotlib, scikit-learn, ...>
activate pg_env
pip install . # or add to PYTHONPATH
pip install pygauss

If you encounter difficulties it may be useful for you to look in working_conda_environments at conda environments known to work.

Single Molecule Analysis

A molecule can be created containg data about the inital geometry, optimisation process and analysis of the final configuration. Molecules can be viewed statically or interactively (not currently supported by Firefox).

mol = pg.molecule.Molecule(folder,
                init_fname='CJS1_emim-cl_B_init.com',
                opt_fname=['CJS1_emim-cl_B_6-311+g-d-p-_gd3bj_opt-modredundant_difrz.log',
                           'CJS1_emim-cl_B_6-311+g-d-p-_gd3bj_opt-modredundant_difrz_err.log',
                           'CJS1_emim-cl_B_6-311+g-d-p-_gd3bj_opt-modredundant_unfrz.log'],
                freq_fname='CJS1_emim-cl_B_6-311+g-d-p-_gd3bj_freq_unfrz.log',
                nbo_fname='CJS1_emim-cl_B_6-311+g-d-p-_gd3bj_pop-nbo-full-_unfrz.log',
                alignto=[3,2,1])

#mol.show_initial(active=True)
display(mol.show_initial(zoom=0.5, rotations=[[0,0,90], [-90, 90, 0]]))
display(mol.show_optimisation(ball_stick=True, rotations=[[0,0,90], [-90, 90, 0]]))

Basic analysis of optimisation…

print('Optimised? {0}, Conformer? {1}, Energy = {2} a.u.'.format(
    mol.is_optimised(), mol.is_conformer(), round(mol.get_optimisation_E(units='hartree'),3)))
ax = mol.plot_optimisation_E(units='hartree')
ax.get_figure().set_size_inches(3, 2)

Optimised? True, Conformer? True, Energy = -805.105 a.u.

Geometric analysis…

print 'Cl optimised polar coords from aromatic ring : ({0}, {1},{2})'.format(
    *[round(i, 2) for i in mol.calc_polar_coords_from_plane(20,3,2,1)])
ax = mol.plot_opt_trajectory(20, [3,2,1])
ax.set_title('Cl optimisation path')
ax.get_figure().set_size_inches(4, 3)

Cl optimised polar coords from aromatic ring : (0.11, -116.42,-170.06)

Potential Energy Scan analysis of geometric conformers…

mol2 = pg.molecule.Molecule(folder, alignto=[3,2,1],
            pes_fname=['CJS_emim_6311_plus_d3_scan.log',
                       'CJS_emim_6311_plus_d3_scan_bck.log'])
ax = mol2.plot_pes_scans([1,4,9,10], rotation=[0,0,90], img_pos='local_maxs', zoom=0.5)
ax.set_title('Ethyl chain rotational conformer analysis')
ax.get_figure().set_size_inches(7, 3)

Natural Bond Orbital and Second Order Perturbation Theory analysis…

print '+ve charge centre polar coords from aromatic ring: ({0} {1},{2})'.format(
    *[round(i, 2) for i in mol.calc_nbo_charge_center(3, 2, 1)])
display(mol.show_nbo_charges(ball_stick=True, axis_length=0.4,
                              rotations=[[0,0,90], [-90, 90, 0]]))
display(mol.show_SOPT_bonds(min_energy=15., rotations=[[0, 0, 90]]))

+ve charge centre polar coords from aromatic ring: (0.02 -51.77,-33.15)

Multiple Computations Analysis

Multiple computations, for instance of different starting conformations, can be grouped into an Analysis class.

analysis = pg.analysis.Analysis(folder)
df, errors = analysis.add_runs(headers=['Cation', 'Anion', 'Initial'],
                               values=[['emim'], ['cl'],
                                       ['B', 'BE', 'BM', 'F', 'FE', 'FM']],
            init_pattern='CJS1_{0}-{1}_{2}_init.com',
            opt_pattern='CJS1_{0}-{1}_{2}_6-311+g-d-p-_gd3bj_opt-modredundant_unfrz.log',
            freq_pattern='CJS1_{0}-{1}_{2}_6-311+g-d-p-_gd3bj_freq_unfrz.log',
            nbo_pattern='CJS1_{0}-{1}_{2}_6-311+g-d-p-_gd3bj_pop-nbo-full-_unfrz.log')
print 'Read Errors:', errors

Read Errors: [{'Cation': 'emim', 'Initial': 'FM', 'Anion': 'cl'}]

The methods mentioned for indivdiual molecules can then be applied to all or a subset of these computations.

analysis.add_mol_property_subset('Opt', 'is_optimised', rows=[2,3])
analysis.add_mol_property('Energy (au)', 'get_optimisation_E', units='hartree')
analysis.add_mol_property('Cation chain, $\\psi$', 'calc_dihedral_angle', [1, 4, 9, 10])
analysis.add_mol_property('Cation Charge', 'calc_nbo_charge', range(1, 20))
analysis.add_mol_property('Anion Charge', 'calc_nbo_charge', [20])
analysis.add_mol_property(['Anion-Cation, $r$', 'Anion-Cation, $\\theta$', 'Anion-Cation, $\\phi$'],
                               'calc_polar_coords_from_plane', 3, 2, 1, 20)
analysis

  Anion Cation Initial   Opt  Energy (au)  Cation chain, $psi$  Cation Charge  Anion Charge  Anion-Cation, $r$  Anion-Cation, $theta$  Anion-Cation, $phi$
0    cl   emim       B   NaN     -805.105                80.794          0.888        -0.888              0.420                -123.392               172.515
1    cl   emim      BE   NaN     -805.105                80.622          0.887        -0.887              0.420                -123.449               172.806
2    cl   emim      BM  True     -805.104                73.103          0.874        -0.874              0.420                 124.121              -166.774
3    cl   emim       F  True     -805.118               147.026          0.840        -0.840              0.420                  10.393                 0.728
4    cl   emim      FE   NaN     -805.117                85.310          0.851        -0.851              0.417                 -13.254                -4.873

NEW FEATURE: there is now an option (requiring pdflatex and ghostscript+imagemagik) to output the tables as a latex formatted image.

analysis.get_table(row_index=['Anion', 'Cation', 'Initial'],
                   column_index=['Cation', 'Anion', 'Anion-Cation'],
                   as_image=True, im_exe='convert_pdf')

RadViz is a way of visualizing multi-variate data.

ax = analysis.plot_radviz_comparison('Anion', columns=range(4, 10))

The KMeans algorithm clusters data by trying to separate samples in n groups of equal variance.

kwargs = {'mtype':'optimised', 'align_to':[3,2,1],
            'rotations':[[0, 0, 90], [-90, 90, 0]],
            'axis_length':0.3}
def show_groups(df):
    for cat, gf in df.groupby('Category'):
        print 'Category {0}:'.format(cat)
        mols = analysis.yield_mol_images(rows=gf.index.tolist(), **kwargs)
        for mol, row in zip(mols, gf.index.tolist()):
            print '(row {0})'.format(row)
            display(mol)
show_groups(analysis.calc_kmean_groups('Anion', 'cl', 4, columns=range(4, 10)))

Category 0:
(row 2)

Category 1:
(row 0)

(row 1)

Category 2:
(row 4)

Category 3:
(row 3)

MORE TO COME!!